A vein image enhancement algorithm for the multi-spectral illumination

2013 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2013-10-01 DOI:10.1109/IST.2013.6729716

Zhaoguo Wu, Ya Zhou, Xiaoming Hu, Muqing Zhou, Xiaobing Dai, Xinzhou Li, Danting Wang

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引用次数: 6

Abstract

Subcutaneous vein is invisible to naked eyes, but can be easily identified under NIR (near-infrared) light, which is widely used in the catheter insertion and biometric identification. However, the quality of raw NIR image usually suffers from low contrast due to uneven illumination and individual difference. Most of current methods used to enhance the contrast between veins and surrounding tissue are based on single wavelength NIR LED. In the meaning the difference of individual NIR absorption is ignored, which results in diverse performance among different individuals. In this work, a training-based contrast enhancement algorithm is applied to hand vein images. NiBlack segmentation method is also used in the NIR system in order to get a high contrast binary image. An enhanced NIR image is generated from processing six images acquired under six mono-wavelength of light (730nm, 830nm, 850nm, 880nm, 890nm and 940nm). The experiment shows that the contrast of the enhanced NIR image increased by 4~10 times.

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一种多光谱照明下的静脉图像增强算法

皮下静脉是肉眼看不见的，但在近红外(NIR)光下可以很容易地识别，广泛应用于导管插入和生物识别。然而，由于光照不均匀和个体差异，原始近红外图像的质量通常存在对比度低的问题。目前大多数用于增强静脉和周围组织对比度的方法都是基于单波长近红外LED。这意味着忽略了个体近红外吸收的差异，从而导致不同个体之间的性能差异。本文提出了一种基于训练的手部静脉图像对比度增强算法。为了得到高对比度的二值图像，在近红外系统中还采用了NiBlack分割方法。在6种单波长光(730nm、830nm、850nm、880nm、890nm和940nm)下采集的6幅图像进行处理，生成增强的近红外图像。实验表明，增强后的近红外图像对比度提高了4~10倍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 IEEE International Conference on Imaging Systems and Techniques (IST)

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